{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **1 导入检查数据**\n",
    "    * 1.1 导入数据\n",
    "    * 1.2 异常值检测\n",
    "    * 1.3 训练集与测试集合并\n",
    "    * 1.4 检查null值和缺失值  \n",
    "* **2 特征分析** \n",
    "    * 2.1 数值型\n",
    "    * 2.2 分类型值\n",
    "* **3 填充缺失值** \n",
    "    * 3.1 Age \n",
    "* **4 特征工程** \n",
    "    * 4.1 名字和称谓  \n",
    "    * 4.2 家庭人数\n",
    "    * 4.3 船舱等级\n",
    "    * 4.4 船票\n",
    "* **5 建模** \n",
    "    * 5.1 简单的模型\n",
    "    * 5.2 叠加模型 \n",
    "    * 5.3 预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns \n",
    "%matplotlib inline \n",
    "\n",
    "from collections import Counter \n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, \\\n",
    "                                GradientBoostingClassifier, ExtraTreesClassifier, VotingClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.linear_model import LogisticRegression \n",
    "from sklearn.neighbors import KNeighborsClassifier \n",
    "from sklearn.tree import DecisionTreeClassifier \n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.svm import SVC \n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score, \\\n",
    "                                    cross_val_score, StratifiedKFold,\\\n",
    "                                    learning_curve\n",
    "from sklearn import model_selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 加载并检查数据\n",
    "### 1.1 加载数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('titanic_train.csv')\n",
    "test = pd.read_csv('titanic_test.csv')\n",
    "IDtest = test['PassengerId']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 异常值检测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 合并训练和测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=True'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass sort=False\n",
      "\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A/5 21171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PC 17599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age Cabin Embarked     Fare  \\\n",
       "0  22.0   NaN        S   7.2500   \n",
       "1  38.0   C85        C  71.2833   \n",
       "2  26.0   NaN        S   7.9250   \n",
       "\n",
       "                                                Name  Parch  PassengerId  \\\n",
       "0                            Braund, Mr. Owen Harris      0            1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...      0            2   \n",
       "2                             Heikkinen, Miss. Laina      0            3   \n",
       "\n",
       "   Pclass     Sex  SibSp  Survived            Ticket  \n",
       "0       3    male      1       0.0         A/5 21171  \n",
       "1       1  female      1       1.0          PC 17599  \n",
       "2       3  female      0       1.0  STON/O2. 3101282  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_len = len(train)\n",
    "df = pd.concat(objs=[train, test], axis=0).reset_index(drop=True)\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4 检查null值和缺失值 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age             263\n",
       "Cabin          1014\n",
       "Embarked          2\n",
       "Fare              1\n",
       "Name              0\n",
       "Parch             0\n",
       "PassengerId       0\n",
       "Pclass            0\n",
       "Sex               0\n",
       "SibSp             0\n",
       "Survived        418\n",
       "Ticket            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1309 entries, 0 to 1308\n",
      "Data columns (total 12 columns):\n",
      "Age            1046 non-null float64\n",
      "Cabin          295 non-null object\n",
      "Embarked       1307 non-null object\n",
      "Fare           1308 non-null float64\n",
      "Name           1309 non-null object\n",
      "Parch          1309 non-null int64\n",
      "PassengerId    1309 non-null int64\n",
      "Pclass         1309 non-null int64\n",
      "Sex            1309 non-null object\n",
      "SibSp          1309 non-null int64\n",
      "Survived       891 non-null float64\n",
      "Ticket         1309 non-null object\n",
      "dtypes: float64(3), int64(4), object(5)\n",
      "memory usage: 122.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1046.000000</td>\n",
       "      <td>1308.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>29.881138</td>\n",
       "      <td>33.295479</td>\n",
       "      <td>0.385027</td>\n",
       "      <td>655.000000</td>\n",
       "      <td>2.294882</td>\n",
       "      <td>0.498854</td>\n",
       "      <td>0.383838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.413493</td>\n",
       "      <td>51.758668</td>\n",
       "      <td>0.865560</td>\n",
       "      <td>378.020061</td>\n",
       "      <td>0.837836</td>\n",
       "      <td>1.041658</td>\n",
       "      <td>0.486592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>7.895800</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>328.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>655.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>31.275000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>982.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age         Fare        Parch  PassengerId       Pclass  \\\n",
       "count  1046.000000  1308.000000  1309.000000  1309.000000  1309.000000   \n",
       "mean     29.881138    33.295479     0.385027   655.000000     2.294882   \n",
       "std      14.413493    51.758668     0.865560   378.020061     0.837836   \n",
       "min       0.170000     0.000000     0.000000     1.000000     1.000000   \n",
       "25%      21.000000     7.895800     0.000000   328.000000     2.000000   \n",
       "50%      28.000000    14.454200     0.000000   655.000000     3.000000   \n",
       "75%      39.000000    31.275000     0.000000   982.000000     3.000000   \n",
       "max      80.000000   512.329200     9.000000  1309.000000     3.000000   \n",
       "\n",
       "             SibSp    Survived  \n",
       "count  1309.000000  891.000000  \n",
       "mean      0.498854    0.383838  \n",
       "std       1.041658    0.486592  \n",
       "min       0.000000    0.000000  \n",
       "25%       0.000000    0.000000  \n",
       "50%       0.000000    0.000000  \n",
       "75%       1.000000    1.000000  \n",
       "max       8.000000    1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.特征分析\n",
    "### 2.1 数值型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.heatmap(train[['Survived', 'SibSp', 'Parch', 'Age', 'Fare']].corr(), annot=True, fmt='.2f', cmap='coolwarm')\n",
    "# 似乎只有Fare 与生存率相关"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x24335dc72b0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 探索生存 与 sibsp的关系 \n",
    "sns.factorplot(x='SibSp', y='Survived', data=train, kind='bar', size=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x24335e36630>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Survived 与 Parch 的关系 \n",
    "sns.factorplot(x='Parch', y='Survived', data=train, kind='bar', size=6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 家庭人数少的生存机会较大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Age 与 Survived\n",
    "g = sns.FacetGrid(train, col='Survived')\n",
    "g = g.map(sns.distplot, 'Age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.kdeplot(train['Age'][(train['Survived']==0) & (train['Age'].notnull())], color='Red', shade=True)\n",
    "g = sns.kdeplot(train['Age'][(train['Survived']==1) & (train['Age'].notnull())], color='Blue', shade=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fare \n",
    "# df[df['Fare'].isnull()]\n",
    "df['Fare'].fillna(df['Fare'].median(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Age\n",
    "g = sns.kdeplot(df['Fare'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24335e83080>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Fare'] = df['Fare'].map(lambda i : np.log(i) if i > 1 else 0)\n",
    "sns.kdeplot(df['Fare'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 分类值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24336451c50>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sex\n",
    "sns.barplot(x='Sex', y='Survived', data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    0.742038\n",
       "male      0.188908\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Sex'])['Survived'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x24336434940>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EmQRMRJRJwEREmQRMRJRJwEREmQRMRJQpDRhJCyVtl7RD0spRtv+GpC81t98i6YzKeiKiu8oCRtJ0YC1wITAfWCppfluzS4EHbD8b+BDwz1X1RET3VfZgFgA7bO+0vQ9YDyxua7MY+Fzz/bXAyySpsKaI6KLKqWNnAbtaloeAcw/VxvZ+Sb8ATgH2tjaStAxY1lx8WNL2koqPzam01d0N+sAbun3I8dCT74p/mJT/d/XmuwJ0+WG/r+ttLxxrH5UBM1p1Poo22F4HrBuPoqpIGrTd3+s6JoN8V52b7N9V5SnSEDCnZXk2sOdQbSQdB/wWcH9hTRHRRZUBswmYJ2mupBnAEmCgrc0AcLCPfxFwg+0n9GAiYnIqO0Vq/qayHNgITAeutL1F0mpg0PYA8BngC5J20Oi5LKmqpwsm9CncBJPvqnOT+rtSOgwRUSV38kZEmQRMRJRJwBwjSVdKulfSD3tdy0QnaY6kGyVtk7RF0lt7XdNEJel4Sd+XdHvzu3p3r2s6GvkN5hhJehHwMPB522f2up6JTNJpwGm2b5N0EnAr8Ke2t/a4tAmneUf7ibYflvQU4D+Bt9q+ucelHZH0YI6R7e+Qe3c6Yvtntm9rvn8I2Ebjbu5o44aHm4tPab4mXW8gARM90Xxy/hzglt5WMnFJmi5pM3Av8HXbk+67SsBE10l6GnAd8Je2f9nreiYq2wdsn03jLvgFkibdKXgCJrqq+XvCdcAXbX+l1/VMBrYfBL4FjPlw4USTgImuaf5w+Rlgm+0P9rqeiUxSn6SnN98/FbgA+FFvqzpyCZhjJOlq4HvAcyUNSbq01zVNYOcBfwG8VNLm5utPel3UBHUacKOkO2g81/d121/rcU1HLJepI6JMejARUSYBExFlEjARUSYBExFlEjARUSYBE0dN0oHmpeYfSvqypBMO0/YKSW/vZn3RewmYOBaP2D67+RT5PuCyXhcUE0sCJsbLd4FnA0h6vaQ7mmOZfKG9oaQ3SdrU3H7dwZ6PpFc3e0O3S/pOc93zmuOibG7uc15X/6o4JrnRLo6apIdtP6055cx1wPXAd4CvAOfZ3ivpZNv3S7oCeNj2BySdYvu+5j7eC/zc9kcl3QkstL1b0tNtPyjpo8DNtr/YnJ1iuu1HevIHxxFLDyaOxVObwwkMAvfQeM7opcC1tvcC2B5trJwzJX23GSgXA89rrr8J+KykN9GYiQIaj2H8jaR3As9IuEwulTM7xtT3SHM4gcc1H2gcq1v8WRoj2d0u6RLgfADbl0k6F3glsFnS2bavknRLc91GSW+0fcM4/x1RJD2YGG/fBF4j6RQASSeP0uYk4GfNoRsuPrhS0rNs32J7FY35mOdIeiaw0/ZHaEzUd1b5XxDjJj2YGFfNyfXeB3xb0gHgB8Albc3+nsZIdj8F7qQROADvb/6IKxpBdTuwEnidpEeBYWB1+R8R4yY/8kZEmZwiRUSZBExElEnARESZBExElEnARESZBExElEnARESZ/wVOPyFz7YwWfwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pclass \n",
    "sns.factorplot(x='Pclass', y='Survived', data=df, kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x24335e7c940>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 358.5x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pclass and Sex \n",
    "sns.factorplot(x='Pclass', y='Survived', hue='Sex', data=df, kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Embarked \n",
    "df['Embarked'] = df['Embarked'].fillna('S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x24330e30208>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Embarked \n",
    "sns.factorplot(x='Embarked', y='Survived', data=df, kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x243364da1d0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Embarked , Pclass \n",
    "sns.factorplot('Pclass', col='Embarked', data=df, kind='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x24337678a58>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 358.5x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Age | Sex, Parch, Pclass, SibSp \n",
    "sns.factorplot(y='Age', x='Sex', data=df, kind='box')\n",
    "sns.factorplot(y='Age', x='Pclass', hue='Sex', data=df, kind='box')\n",
    "sns.factorplot(y='Age', x='SibSp', data=df, kind='box')\n",
    "sns.factorplot(y='Age', x='Parch', data=df, kind='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x243377690f0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df[['Age', 'Sex', 'SibSp', 'Parch', 'Pclass']].corr(), cmap='BrBG', annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 填充年龄缺失值 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_title = [i.split(',')[1].split('.')[0].strip() for i in df['Name']]\n",
    "df['Title'] = df_title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x243377ade80>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Title', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Title\n",
       "Capt              1\n",
       "Col               4\n",
       "Don               1\n",
       "Dona              1\n",
       "Dr                8\n",
       "Jonkheer          1\n",
       "Lady              1\n",
       "Major             2\n",
       "Master           61\n",
       "Miss            260\n",
       "Mlle              2\n",
       "Mme               1\n",
       "Mr              757\n",
       "Mrs             197\n",
       "Ms                2\n",
       "Rev               8\n",
       "Sir               1\n",
       "the Countess      1\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Title')['Title'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.replace(['Capt','Major','Col','Don','Dona','Dr','Jonkheer','Mlle','Mme','Ms','Rev', 'the Countess'], 'Rare', inplace=True)\n",
    "df.replace(['Sir'], 'Mr', inplace=True)\n",
    "df.replace(['Lady'], 'Miss', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df['Cabin'] = pd.Series([i[0] if not pd.isnull(i) else 'X' for i in df['Cabin']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24337903a58>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y='Cabin', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x243379406d8>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(y='Survived', x='Cabin', data=df, kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Ticket'] = pd.Series([t.replace('/', '').replace('.','').split(' ')[0] if len(t.split(' '))-1 else 'X' for t in df.Ticket])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Fsize'] = df['SibSp'] + df['Parch']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x243378aecc0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x='Fsize', y='Survived', data=df, kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Sex'] = df['Sex'].map({'female': 0, 'male': 1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:189: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    }
   ],
   "source": [
    "index_Nan_age = list(df['Age'][df['Age'].isnull()].index)\n",
    "for i in index_Nan_age:\n",
    "    age_med = df['Age'].median()\n",
    "    age_pred = df['Age'][((df['SibSp']==df.iloc[i]['SibSp']) & \\\n",
    "                            (df['Parch']==df.iloc[i]['Parch'])&\\\n",
    "                            (df['Pclass']==df.iloc[i]['Pclass']))].median()\n",
    "    if not np.isnan(age_pred):\n",
    "        df['Age'].iloc[i] = age_pred \n",
    "    else:\n",
    "        df['Age'].iloc[i] = age_med"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1309 entries, 0 to 1308\n",
      "Data columns (total 14 columns):\n",
      "Age            1309 non-null float64\n",
      "Cabin          1309 non-null object\n",
      "Embarked       1309 non-null object\n",
      "Fare           1309 non-null float64\n",
      "Name           1309 non-null object\n",
      "Parch          1309 non-null int64\n",
      "PassengerId    1309 non-null int64\n",
      "Pclass         1309 non-null int64\n",
      "Sex            1309 non-null int64\n",
      "SibSp          1309 non-null int64\n",
      "Survived       891 non-null float64\n",
      "Ticket         1309 non-null object\n",
      "Title          1309 non-null object\n",
      "Fsize          1309 non-null int64\n",
      "dtypes: float64(3), int64(6), object(5)\n",
      "memory usage: 143.2+ KB\n",
      "['X' 'C' 'E' 'G' 'D' 'A' 'B' 'F' 'T']\n",
      "['S' 'C' 'Q']\n",
      "['A5' 'PC' 'STONO2' 'X' 'PP' 'CA' 'SCParis' 'SCA4' 'A4' 'SP' 'SOC' 'WC'\n",
      " 'SOTONOQ' 'WEP' 'STONO' 'C' 'SCPARIS' 'SOP' 'Fa' 'FCC' 'SWPP' 'SCOW'\n",
      " 'PPP' 'SC' 'SCAH' 'AS' 'SOPP' 'FC' 'SOTONO2' 'CASOTON' 'SCA3' 'STONOQ'\n",
      " 'AQ4' 'A' 'LP' 'AQ3']\n",
      "['Mr' 'Mrs' 'Miss' 'Master' 'Rare']\n"
     ]
    }
   ],
   "source": [
    "df.info()\n",
    "print(df.Cabin.unique())\n",
    "print(df.Embarked.unique())\n",
    "print(df.Ticket.unique())\n",
    "print(df.Title.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.get_dummies(df, columns=['Cabin', 'Embarked','Ticket', 'Title'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['PassengerId', 'Name'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Fsize</th>\n",
       "      <th>Cabin_A</th>\n",
       "      <th>Cabin_B</th>\n",
       "      <th>...</th>\n",
       "      <th>Ticket_STONOQ</th>\n",
       "      <th>Ticket_SWPP</th>\n",
       "      <th>Ticket_WC</th>\n",
       "      <th>Ticket_WEP</th>\n",
       "      <th>Ticket_X</th>\n",
       "      <th>Title_Master</th>\n",
       "      <th>Title_Miss</th>\n",
       "      <th>Title_Mr</th>\n",
       "      <th>Title_Mrs</th>\n",
       "      <th>Title_Rare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>1.981001</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>4.266662</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
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       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>2.085672</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age      Fare  Parch  Pclass  Sex  SibSp  Survived  Fsize  Cabin_A  \\\n",
       "0  22.0  1.981001      0       3    1      1       0.0      1        0   \n",
       "1  38.0  4.266662      0       1    0      1       1.0      1        0   \n",
       "2  26.0  2.070022      0       3    0      0       1.0      0        0   \n",
       "3  35.0  3.972177      0       1    0      1       1.0      1        0   \n",
       "4  35.0  2.085672      0       3    1      0       0.0      0        0   \n",
       "\n",
       "   Cabin_B     ...      Ticket_STONOQ  Ticket_SWPP  Ticket_WC  Ticket_WEP  \\\n",
       "0        0     ...                  0            0          0           0   \n",
       "1        0     ...                  0            0          0           0   \n",
       "2        0     ...                  0            0          0           0   \n",
       "3        0     ...                  0            0          0           0   \n",
       "4        0     ...                  0            0          0           0   \n",
       "\n",
       "   Ticket_X  Title_Master  Title_Miss  Title_Mr  Title_Mrs  Title_Rare  \n",
       "0         0             0           0         1          0           0  \n",
       "1         0             0           0         0          1           0  \n",
       "2         0             0           1         0          0           0  \n",
       "3         1             0           0         0          1           0  \n",
       "4         1             0           0         1          0           0  \n",
       "\n",
       "[5 rows x 61 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3694: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  errors=errors)\n"
     ]
    }
   ],
   "source": [
    "train = df[:train_len]\n",
    "test = df[train_len:]\n",
    "test.drop('Survived', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_train = train['Survived']\n",
    "X_train = train.drop('Survived', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([0.8       , 0.86666667, 0.76404494, 0.87640449, 0.83146067,\n",
      "       0.7752809 , 0.80898876, 0.80898876, 0.87640449, 0.84090909]), array([0.73333333, 0.78888889, 0.76404494, 0.82022472, 0.82022472,\n",
      "       0.86516854, 0.83146067, 0.74157303, 0.83146067, 0.81818182]), array([0.72222222, 0.84444444, 0.75280899, 0.85393258, 0.80898876,\n",
      "       0.83146067, 0.80898876, 0.74157303, 0.85393258, 0.875     ]), array([0.77777778, 0.83333333, 0.78651685, 0.83146067, 0.79775281,\n",
      "       0.85393258, 0.79775281, 0.7752809 , 0.85393258, 0.82954545]), array([0.81111111, 0.81111111, 0.76404494, 0.83146067, 0.80898876,\n",
      "       0.85393258, 0.80898876, 0.76404494, 0.82022472, 0.84090909]), array([0.81111111, 0.82222222, 0.75280899, 0.88764045, 0.86516854,\n",
      "       0.80898876, 0.84269663, 0.78651685, 0.87640449, 0.85227273]), array([0.62222222, 0.61111111, 0.7752809 , 0.83146067, 0.83146067,\n",
      "       0.76404494, 0.80898876, 0.79775281, 0.86516854, 0.85227273]), array([0.72222222, 0.76666667, 0.75280899, 0.82022472, 0.79775281,\n",
      "       0.73033708, 0.84269663, 0.7752809 , 0.85393258, 0.82954545]), array([0.82222222, 0.84444444, 0.79775281, 0.87640449, 0.82022472,\n",
      "       0.75280899, 0.82022472, 0.80898876, 0.87640449, 0.86363636]), array([0.77777778, 0.81111111, 0.79775281, 0.88764045, 0.85393258,\n",
      "       0.79775281, 0.82022472, 0.75280899, 0.87640449, 0.86363636])]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Cross validation scores')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kfold = StratifiedKFold(n_splits=10)\n",
    "random_state = 2 \n",
    "classifiers = []\n",
    "classifiers.append(SVC(random_state=random_state))\n",
    "classifiers.append(DecisionTreeClassifier(random_state=random_state))\n",
    "classifiers.append(AdaBoostClassifier(DecisionTreeClassifier(random_state=random_state),random_state=random_state,learning_rate=0.1))\n",
    "classifiers.append(RandomForestClassifier(random_state=random_state))\n",
    "classifiers.append(ExtraTreesClassifier(random_state=random_state))\n",
    "classifiers.append(GradientBoostingClassifier(random_state=random_state))\n",
    "classifiers.append(MLPClassifier(random_state=random_state))\n",
    "classifiers.append(KNeighborsClassifier())\n",
    "classifiers.append(LogisticRegression(random_state=random_state))\n",
    "classifiers.append(LinearDiscriminantAnalysis())\n",
    "\n",
    "cv_results = []\n",
    "for classifier in classifiers:\n",
    "    cv_results.append(cross_val_score(classifier, X_train, y=Y_train, scoring='accuracy',cv=kfold, n_jobs=-1))\n",
    "\n",
    "print(cv_results)\n",
    "cv_means = []\n",
    "cv_std = []\n",
    "for cv_result in cv_results:\n",
    "    cv_means.append(cv_result.mean())\n",
    "    cv_std.append(cv_result.std())\n",
    "\n",
    "cv_res = pd.DataFrame({'CrossValMeans':cv_means, 'CrossValerrors': cv_std, 'Algorithm':['SVC', 'DecisionTree', 'AdaBoost', 'RandomForest',\n",
    "                                                                                       'ExtraTrees', 'GradientBoosting', 'MultipleLayerPerceptron',\n",
    "                                                                                       'KNeighboors','LogisticRegression', 'LinearDiscriminantAnalysis']})\n",
    "g = sns.barplot('CrossValMeans', 'Algorithm', data=cv_res, palette='Set3', orient='h', **{'xerr': cv_std})\n",
    "g.set_xlabel('Mean Accuracy')\n",
    "g.set_title('Cross validation scores')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 160 candidates, totalling 1600 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed:   10.9s\n",
      "[Parallel(n_jobs=-1)]: Done 196 tasks      | elapsed:   31.1s\n",
      "[Parallel(n_jobs=-1)]: Done 639 tasks      | elapsed:  1.4min\n",
      "[Parallel(n_jobs=-1)]: Done 1339 tasks      | elapsed:  2.8min\n",
      "[Parallel(n_jobs=-1)]: Done 1597 out of 1600 | elapsed:  3.3min remaining:    0.3s\n",
      "[Parallel(n_jobs=-1)]: Done 1600 out of 1600 | elapsed:  3.3min finished\n"
     ]
    }
   ],
   "source": [
    "# 调节hyperparameter 找到最合适的模型\n",
    "DTC = DecisionTreeClassifier()\n",
    "adaDTC = AdaBoostClassifier(DTC, random_state=7)\n",
    "ada_param_grid = {'base_estimator__criterion':['gini', 'entropy',],\n",
    "                'base_estimator__splitter': ['best', 'random'],\n",
    "                'algorithm': ['SAMME', 'SAMME.R'],\n",
    "                'n_estimators': [10,50],\n",
    "                'learning_rate':[0.0001,0.0003,0.001,0.003,0.01,0.03,0.1,0.3,1,3]\n",
    "                }\n",
    "\n",
    "adaDTC = GridSearchCV(adaDTC, param_grid=ada_param_grid, cv=kfold, scoring='accuracy',n_jobs=-1, verbose=1)\n",
    "adaDTC.fit(X_train, Y_train)\n",
    "ada_best = adaDTC.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8305274971941639"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adaDTC.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 54 candidates, totalling 540 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed:   21.7s\n",
      "[Parallel(n_jobs=-1)]: Done 196 tasks      | elapsed:  1.1min\n",
      "[Parallel(n_jobs=-1)]: Done 446 tasks      | elapsed:  2.6min\n",
      "[Parallel(n_jobs=-1)]: Done 540 out of 540 | elapsed:  3.2min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8305274971941639"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 调节 ExtraTrees 找到最好的模型 \n",
    "ExtC = ExtraTreesClassifier()\n",
    "\n",
    "# search grid for optimal parameters\n",
    "ex_param_grid = {\n",
    "    'max_depth': [None],\n",
    "    'max_features':[1,3,10],\n",
    "    'min_samples_split': [2,3,10],\n",
    "    'min_samples_leaf': [1,3,10],\n",
    "    'bootstrap': [False],\n",
    "    'n_estimators': [100,300],\n",
    "    'criterion':['gini']\n",
    "}\n",
    "\n",
    "gsExtC = GridSearchCV(ExtC, param_grid=ex_param_grid, cv=kfold, scoring='accuracy', n_jobs=-1, verbose=1)\n",
    "gsExtC.fit(X_train, Y_train)\n",
    "ExtC_best = gsExtC.best_estimator_\n",
    "gsExtC.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 54 candidates, totalling 540 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed:   27.3s\n",
      "[Parallel(n_jobs=-1)]: Done 196 tasks      | elapsed:  1.3min\n",
      "[Parallel(n_jobs=-1)]: Done 446 tasks      | elapsed:  3.1min\n",
      "[Parallel(n_jobs=-1)]: Done 540 out of 540 | elapsed:  3.7min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8383838383838383"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 调节随机森林参数 找到最好的模型 \n",
    "RFC = RandomForestClassifier()\n",
    "\n",
    "rf_param_grid = {'max_depth':[None],\n",
    "                'max_features': [1,3,10],\n",
    "                'min_samples_split':[2,3,10],\n",
    "                'min_samples_leaf':[1,3,10],\n",
    "                'bootstrap':[False],\n",
    "                'n_estimators':[100,300],\n",
    "                'criterion':['gini']}\n",
    "gsRFC = GridSearchCV(RFC, param_grid=rf_param_grid, cv=kfold, scoring='accuracy', n_jobs=-1, verbose=1)\n",
    "gsRFC.fit(X_train, Y_train)\n",
    "RFC_best = gsRFC.best_estimator_\n",
    "gsRFC.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 72 candidates, totalling 720 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed:   12.9s\n",
      "[Parallel(n_jobs=-1)]: Done 196 tasks      | elapsed:   32.2s\n",
      "[Parallel(n_jobs=-1)]: Done 446 tasks      | elapsed:  1.1min\n",
      "[Parallel(n_jobs=-1)]: Done 720 out of 720 | elapsed:  1.7min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8282828282828283"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# gradient boosting tunning\n",
    "GBC = GradientBoostingClassifier()\n",
    "gb_param_grid = {'loss': ['deviance'],\n",
    "                'n_estimators':[100, 200, 300],\n",
    "                'learning_rate':[0.1, 0.05, 0.01],\n",
    "                'max_depth':[4, 8],\n",
    "                'min_samples_leaf':[100, 150],\n",
    "                'max_features':[0.3, 0.1]}\n",
    "gsGBC = GridSearchCV(GBC, param_grid=gb_param_grid, cv=kfold, scoring='accuracy',n_jobs=-1, verbose=1)\n",
    "gsGBC.fit(X_train, Y_train)\n",
    "GBC_best = gsGBC.best_estimator_\n",
    "gsGBC.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 28 candidates, totalling 280 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed:   16.5s\n",
      "[Parallel(n_jobs=-1)]: Done 196 tasks      | elapsed:   57.2s\n",
      "[Parallel(n_jobs=-1)]: Done 280 out of 280 | elapsed:  1.6min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8361391694725028"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# SVC classifier \n",
    "SVMC = SVC(probability=True)\n",
    "svc_param_grid = {'kernel':['rbf'],\n",
    "                 'gamma': [0.001,0.01,0.1,1],\n",
    "                 'C':[1,10,50,100,200,300,1000]}\n",
    "gsSVMC = GridSearchCV(SVMC, param_grid=svc_param_grid, cv=kfold, scoring='accuracy',n_jobs=-1, verbose=1)\n",
    "gsSVMC.fit(X_train, Y_train)\n",
    "SVMC_best = gsSVMC.best_estimator_\n",
    "gsSVMC.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 画出学习曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None, n_jobs=-1, train_sizes=np.linspace(0.1,1,5)):\n",
    "    plt.figure()\n",
    "    plt.title(title)\n",
    "    if ylim is not None:\n",
    "        plt.ylim(*ylim)\n",
    "    plt.xlabel(\"Training examples\")\n",
    "    plt.ylabel(\"Score\")\n",
    "    train_sizes, train_scores, test_scores = learning_curve(estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "    plt.grid()\n",
    "    plt.fill_between(train_sizes, train_scores_mean-train_scores_std, train_scores_mean+train_scores_std, alpha=0.1, color='r')\n",
    "    plt.fill_between(train_sizes, test_scores_mean-test_scores_std, test_scores_mean+test_scores_std, alpha=0.1, color='g')\n",
    "    plt.plot(train_sizes, train_scores_mean, 'o-', color='r', label='Training score')\n",
    "    plt.plot(train_sizes, test_scores_mean, 'o-', color='g', label='Cross-validation score')\n",
    "    plt.legend(loc='best')\n",
    "    return plt \n",
    "\n",
    "g = plot_learning_curve(gsRFC.best_estimator_, 'RF mearning curves', X_train, Y_train, cv=kfold)\n",
    "g = plot_learning_curve(gsExtC.best_estimator_, 'ExtraTrees learning curves', X_train, Y_train, cv=kfold)\n",
    "g = plot_learning_curve(gsSVMC.best_estimator_, 'SVC learning curves', X_train, Y_train, cv=kfold)\n",
    "g = plot_learning_curve(adaDTC.best_estimator_, 'AdaBoost learning curves', X_train, Y_train, cv=kfold)\n",
    "g = plot_learning_curve(gsGBC.best_estimator_, 'GradientBoosting learning curves', X_train, Y_train, cv=kfold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ensemble modeling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "votingC = VotingClassifier(estimators=[('rfc', RFC_best), ('extc', ExtC_best),\n",
    "                                     ('svc', SVMC_best), ('adac', ada_best),\n",
    "                                     ('gbc',GBC_best)], voting='soft', n_jobs=4)\n",
    "votingC = votingC.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Voyager\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    }
   ],
   "source": [
    "test_survived = pd.Series(votingC.predict(test), name='Survived')\n",
    "results = pd.concat([IDtest, test_survived], axis=1)\n",
    "results.to_csv('ensemble_python_voting.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = plot_learning_curve(votingC, 'Ensemble learning curves', X_train, Y_train, cv=kfold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extratree \n",
    "ExtC_survived = pd.Series(ExtC_best.predict(test), name='Survived')\n",
    "results = pd.concat([IDtest, ExtC_survived], axis=1)\n",
    "results.to_csv('extraTree_submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for cv_result in cv_results:\n",
    "    print(cv_result.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for i in range(2,10):\n",
    "#     for j in range(2, 10):\n",
    "rf = RandomForestClassifier(random_state=2, n_estimators=500, min_samples_split=7, min_samples_leaf=3)\n",
    "kf = model_selection.KFold(n_splits=5, shuffle=False, random_state=2)\n",
    "scores = model_selection.cross_val_score(rf, X_train, Y_train, cv=kf)\n",
    "#     print(scores)\n",
    "# print(i, j, scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 150 candidates, totalling 1500 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed:   23.1s\n",
      "[Parallel(n_jobs=-1)]: Done 196 tasks      | elapsed:  1.2min\n",
      "[Parallel(n_jobs=-1)]: Done 446 tasks      | elapsed:  2.4min\n",
      "[Parallel(n_jobs=-1)]: Done 796 tasks      | elapsed:  4.3min\n",
      "[Parallel(n_jobs=-1)]: Done 1246 tasks      | elapsed:  6.8min\n",
      "[Parallel(n_jobs=-1)]: Done 1500 out of 1500 | elapsed:  8.3min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=3, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=300, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kfold = StratifiedKFold(n_splits=10)\n",
    "rf = RandomForestClassifier()\n",
    "rf_params = {\n",
    "    'max_features':['auto', 'sqrt', 0.33],\n",
    "    'min_samples_split':[2,3,5,7,10],\n",
    "    'min_samples_leaf':[1,3,5,7,10],\n",
    "    'n_estimators': [100, 300],\n",
    "    'criterion': ['gini']}\n",
    "gsrf = GridSearchCV(rf, param_grid=rf_params, cv=kfold, scoring='accuracy', n_jobs=-1, verbose=1)\n",
    "gsrf.fit(X_train, Y_train)\n",
    "gsrf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8383838383838383"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsrf.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=3, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=300, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsrf_best = gsrf.best_estimator_\n",
    "gsrf_best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_survived = gsrf_best.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.read_csv('submission.csv')\n",
    "submission['Survived'] = test_survived.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv('submission_08_26.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
